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Documents authored by Liu, Cong

Document
DOI: 10.4230/LIPIcs.ECRTS.2024.14
GCAPS: GPU Context-Aware Preemptive Priority-Based Scheduling for Real-Time Tasks

Authors: Yidi Wang, Cong Liu, Daniel Wong, and Hyoseung Kim

Published in: LIPIcs, Volume 298, 36th Euromicro Conference on Real-Time Systems (ECRTS 2024)

Abstract
Scheduling real-time tasks that utilize GPUs with analyzable guarantees poses a significant challenge due to the intricate interaction between CPU and GPU resources, as well as the complex GPU hardware and software stack. While much research has been conducted in the real-time research community, several limitations persist, including the absence or limited availability of GPU-level preemption, extended blocking times, and/or the need for extensive modifications to program code. In this paper, we propose GCAPS, a GPU Context-Aware Preemptive Scheduling approach for real-time GPU tasks. Our approach exerts control over GPU context scheduling at the device driver level and enables preemption of GPU execution based on task priorities by simply adding one-line macros to GPU segment boundaries. In addition, we provide a comprehensive response time analysis of GPU-using tasks for both our proposed approach as well as the default Nvidia GPU driver scheduling that follows a work-conserving round-robin policy. Through empirical evaluations and case studies, we demonstrate the effectiveness of the proposed approaches in improving taskset schedulability and response time. The results highlight significant improvements over prior work as well as the default scheduling approach, with up to 40% higher schedulability, while also achieving predictable worst-case behavior on Nvidia Jetson embedded platforms.
Cite as

Yidi Wang, Cong Liu, Daniel Wong, and Hyoseung Kim. GCAPS: GPU Context-Aware Preemptive Priority-Based Scheduling for Real-Time Tasks. In 36th Euromicro Conference on Real-Time Systems (ECRTS 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 298, pp. 14:1-14:25, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{wang_et_al:LIPIcs.ECRTS.2024.14,
  author =	{Wang, Yidi and Liu, Cong and Wong, Daniel and Kim, Hyoseung},
  title =	{{GCAPS: GPU Context-Aware Preemptive Priority-Based Scheduling for Real-Time Tasks}},
  booktitle =	{36th Euromicro Conference on Real-Time Systems (ECRTS 2024)},
  pages =	{14:1--14:25},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-324-9},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{298},
  editor =	{Pellizzoni, Rodolfo},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECRTS.2024.14},
  URN =		{urn:nbn:de:0030-drops-203170},
  doi =		{10.4230/LIPIcs.ECRTS.2024.14},
  annote =	{Keywords: Real-time systems, GPU scheduling}
}
Document
DOI: 10.4230/LIPIcs.ECRTS.2017.15
Optimal Dataflow Scheduling on a Heterogeneous Multiprocessor With Reduced Response Time Bounds

Authors: Zheng Dong, Cong Liu, Alan Gatherer, Lee McFearin, Peter Yan, and James H. Anderson

Published in: LIPIcs, Volume 76, 29th Euromicro Conference on Real-Time Systems (ECRTS 2017)

Abstract
Heterogeneous computing platforms with multiple types of computing resources have been widely used in many industrial systems to process dataflow tasks with pre-defined affinity of tasks to subgroups of resources. For many dataflow workloads with soft real-time requirements, guaranteeing fast and bounded response times is often the objective. This paper presents a new set of analysis techniques showing that a classical real-time scheduler, namely earliest-deadline first (EDF), is able to support dataflow tasks scheduled on such heterogeneous platforms with provably bounded response times while incurring no resource capacity loss, thus proving EDF to be an optimal solution for this scheduling problem. Experiments using synthetic workloads with widely varied parameters also demonstrate that the magnitude of the response time bounds yielded under the proposed analysis is reasonably small under all scenarios. Compared to the state-of-the-art soft real-time analysis techniques, our test yields a 68% reduction on response time bounds on average. This work demonstrates the potential of applying EDF into practical industrial systems containing dataflow-based workloads that desire guaranteed bounded response times.
Cite as

Zheng Dong, Cong Liu, Alan Gatherer, Lee McFearin, Peter Yan, and James H. Anderson. Optimal Dataflow Scheduling on a Heterogeneous Multiprocessor With Reduced Response Time Bounds. In 29th Euromicro Conference on Real-Time Systems (ECRTS 2017). Leibniz International Proceedings in Informatics (LIPIcs), Volume 76, pp. 15:1-15:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)

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@InProceedings{dong_et_al:LIPIcs.ECRTS.2017.15,
  author =	{Dong, Zheng and Liu, Cong and Gatherer, Alan and McFearin, Lee and Yan, Peter and Anderson, James H.},
  title =	{{Optimal Dataflow Scheduling on a Heterogeneous Multiprocessor With Reduced Response Time Bounds}},
  booktitle =	{29th Euromicro Conference on Real-Time Systems (ECRTS 2017)},
  pages =	{15:1--15:22},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-037-8},
  ISSN =	{1868-8969},
  year =	{2017},
  volume =	{76},
  editor =	{Bertogna, Marko},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECRTS.2017.15},
  URN =		{urn:nbn:de:0030-drops-71565},
  doi =		{10.4230/LIPIcs.ECRTS.2017.15},
  annote =	{Keywords: Real-time Scheduling, schedulability, heterogeneous multiprocessor}
}
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